Data

UBI models failed. So, now what?

19 Sep 2019

The auto insurance industry is at an economic crisis.

Faced with flagging revenues and steadily worsening loss ratios, many insurers have rushed to incorporate usage-based insurance (UBI) models into their offerings in order to limit their exposure to risk and provide a better customer experience.

But several years into these initiatives, far from saving the auto insurance industry, these UBI models seem to be creating new financial problems for providers, and even exacerbating old ones.

Is there an issue with usage-based insurance in general? Or could correcting these trends simply be a matter of adding a piece of the puzzle that’s currently missing?

Loss Ratios Still Plaguing UBI Models

While usage-based insurance models were hailed as saviors for the struggling auto insurance industry when they were introduced, that simply hasn’t been the case.

State Farm, for example, reports that its costs-per-claim have risen by a staggering 35 percent since 2010. Progressive, creator of the well-known “Snapshot” UBI dongle, has seen a similar increase of 5 percent each year since 2015. Deke Phillips of CCC Information Services claims that many insurers will need UBI loss ratios to fall by as much as 25 percent just to “break even.”

UBI models seek to take the guesswork out of underwriting risks by replacing generic proxy variables with specific, real-world data. Why, then, are they having the opposite effect for auto insurers?

Driver Distraction: The Real Risk Multiplier

The primary cause of this discrepancy between the reality of UBI models and their expected benefit is a single major blindspot: distracted driving.

UBI models do indeed account for traditional risk multipliers like rapid acceleration, excessive speeding, and hard braking. But these markers don’t properly account for the modern driver.

The ubiquity of smartphones and tablets has caused a seismic, and likely permanent, shift in the behavior of drivers and the sources of in-vehicle risk. Zendrive, a smartphone-based telematics platform, recently conducted the largest study of distracted driving in American history. It concluded that 60 percent of U.S. drivers use their phones while behind the wheel every day. And as many as 40 percent of drivers check their phones at least once an hour. In fact, the average distracted driver spends 3 minutes and 40 seconds looking at his or her phone each hour. While traveling at 55 miles per hour, that equates to traveling the equivalent distance of 42 football fields blindfolded.

Even back in 2014, when smartphones were far more primitive than they are today, the National Highway Traffic Safety Administration (NHTSA) reported that 14 percent of police-documented traffic collisions were labeled “distraction affected” as their primary cause. With the coming advent of 5G, which promises data speeds of 10 Gbps – nearly 5,000 times faster than current 4G LTE standards – drivers will soon become even more distracted by the smart devices they carry with them everywhere.

The Zendrive study concluded that distracted driving is as much as 100 times more prevalent than current NHTSA estimates. And that the number of drivers who spend greater than 25 percent of their time behind the wheel distracted by their smart devices has more than doubled since 2018 alone.

But traditional UBI insurance models cannot account for distracted driving. Onboard diagnostics (OBD), whether plug-in dongles or OEM hardware, measure only what the vehicle is doing, not what the driver is doing.

This is precisely the reason that UBI models are struggling under unsustainable loss ratios, and why a better predictive model is needed.

The Future: Behavior-Based Insurance Models

The solution is a model that is able to measure both vehicle usage and driver behavior, especially driver distraction.

These modern “behavior-based” insurance models (BBI) leverage advanced AI, machine learning, and the highly accurate sensors featured on most smartphones to paint a full 360-degree picture of a driver’s in-vehicle risk. These smartphone-based solutions not only provide telematics data that is just as accurate as OBD devices according to independent analysts, but BBI models that measure and take device usage/distracted driving into account are up to 6X better at predicting collisions, risk, and loss.

Yet the average driver premium increase for receiving a ticket for mobile phone use is still 35 percent less than the average premium increase for driving too slowly – a relatively innocuous violation. Clearly, the industry needs to catch up to the new realities of the market, and the benefits of behavior-based insurance.

Because BBI models can predict risk with 6 times the accuracy of traditional models, they empower insurers to price their lines more accurately, identify and decline risky lines more accurately, and yield much, much more favorable loss ratios.

A Better Way Forward

At Fairmatic, we believe this is the direction the auto insurance industry is headed as it struggles to reverse unsustainable loss ratios. Especially since implementing BBI solutions for insurers is as easy as downloading a smartphone application; there is no additional investment in hardware required.

And with BBI models growing smarter and more accurate every month as they gather more real-world data, the future for auto insurance once again promises to be a profitable one.

Rhea Harris

General Manager